ISNN 2014 : 11th International Symposium on Neural Networks

Страна: Китай

Город: Hong Kong, Macao

Тезисы до: 01.07.2014

Даты: 28.11.14 — 01.11.14

Е-мейл Оргкомитета: snn@mae.cuhk.edu.hk

Организаторы: The Chinese University of Hong Kong, Hong Kong

 

The Eleventh International Symposium on Neural Networks (ISNN 2014) will be held in Hong Kong and Macao, the twin city, following the successes of previous events. Known as the Special Administrative Regions of China, Hong Kong and Macao are two modern metropolis situated on the southern coast of China by the Pearl River Delta.

ISNN 2014 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and special sessions focusing on popular topics.

Prospective authors are invited to contribute high-quality papers to ISNN 2014. In addition, proposals for special sessions within the technical scopes of the symposium are solicited. Special sessions, to be organized by internationally recognized experts, aim to bring together researchers in special focused topics. Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the contributed papers. Researchers interested in organizing special sessions are invited to submit formal proposals to ISNN 2014. A special session proposal should include the session title, a brief description of the scope and motivation, names, contact information and brief biographical information of the organizers.

Topics areas include, but not limited to, computational neuroscience, connectionist theory and cognitive science, mathematical modeling of neural systems, neurodynamic analysis, neurodynamic optimization and adaptive dynamic programming, embedded neural systems, probabilistic and information-theoretic methods, principal and independent component analysis, hybrid intelligent systems, supervised, unsupervised and reinforcement learning, brain imaging and neural information processing, neuroinformatics and bioinformatics, support vector machines and kernel methods, autonomous mental development, data mining, pattern recognition, time series analysis, image and signal processing, robotic and control applications, telecommunications, transportation systems, intrusion detection and fault diagnosis, hardware implementation, real-world applications.

Authors are invited to submit full-length papers (10 pages maximum) by the submission deadline through the online submission system. Potential organizers are also invited to enlist five or more papers with cohesive topics to form special sessions. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality, and clarity. The authors of accepted papers will have an opportunity to revise their papers and take consideration of the referees' comments and suggestions. Papers presented at ISNN 2014 will be published in the EI-indexed proceedings in the Springer LNCS series and selected good papers will be included in special issues of several SCI journals.

  Papers are solicited for, but not limited to the following tracks:

 

  • Computational Neuroscience and Cognitive Science
  •   Computational Neural Models
      Spiking Neurons
      Visual and Auditory Cortex
      Neural Encoding and Decoding
      Plasticity and Adaptation
      Brain Imaging (fMRI, MEG, EEG)
      Learning and Memory
      Inference and Reasoning
      Knowledge Acquisition and Language
      Perception, Emotion and Development
      Action and Motor Control
      Attractor and Associative Memory
      Neurodynamics, Complex Systems, and Chaos

     

  • Models, Methods and Algorithms
  •   Stability and Convergence Analysis
      Neural Network Models (Feedforward/Recurrent/Self-organizing/Cellular/Hybrid Neural Networks)
      Supervised/Unsupervised/Reinforcement Learning
      Statistical Learning Algorithms (PCA, ICA, Projection Pursuit Methods)
      Kernel Methods, Large Margin Methods and SVM
      Optimization Algorithms / Variational Methods
      Probabilistic and Information-Theoretic Methods
      Mixture Models, Graphical Models, Topic Models and Gaussian Processes
      Ensemble Learning, Committee Algorithms and Boosting
      Bayesian, Belief, Causal and Semantic Networks
      Model Selection and Structure Learning
      Feature Analysis and Clustering
      Sparsity and Feature Selection
      Pattern Analysis and Classification
      Matrix/Tensor Analysis and Factorization
      Temporal Models and Sequence Data
      Structured and Relational Data
      Embeddings and Manifold Learning
      Active Learning

     

  • Vision and Auditory Modelling
  •   Visual Perception and Modelling
      Visual Selective Attention
      Statistical and Pattern Recognition
      Visual Features Analysis
      Object Recognition
      Motion and Tracking
      Natural Scene Statistics
      Image Segmentation
      Image Coding and Representation
      Auditory Perception and Modeling
      Source Separation
      Speech Recognition and Speech Synthesis
      Speaker Identification
      Audio and Speech Retrieval
      Music Modeling and Analysis

     

  • Control, Robotics and Hardware
  •   Neuromorphic Hardware and Implementations
      Embedded Neural Networks
      Reconfigurable Systems
      Fuzzy Neural Networks
      Robotics: Neural Robotics, Cognitive Robotics, Developmental Robotics
      Multi-Agent Systems and Game Theory
      Reinforcement Learning
      Markov Decision Processes
      Planning and Decision Making
      Predictive State Representations
      Policy Search
      Action and Motor Control
      Visuomotor Control

     

  • Novel Approaches and Applications
  •   Brain-Like Systems, Adaptive Intelligent Systems
      Brain-Computer Interfaces
      Granular Computing
      Evolutionary Neural Networks
      Hybrid Intelligent Systems
      Bioinformatics and Biomedical Engineering
      Neuroinformatics and Neuroengineering
      Systems Biology
      Time Series Prediction
      Information Retrieval
      Data Mining and Knowledge Discovery
      Natural Language Processing

Веб-сайт конференции: http://isnn.mae.cuhk.edu.hk/